In neighborhood aggregation schemes, each vertex in a graph is assigned
a color or attribute based on a summary of the local structure
surrounding the vertex. For each vertex, iteratively, the attributes /
colors are aggregated to compute a new attribute / color that eventually
represents the structure of its extended neighborhood in a compressed
and compact form.
Shervashidze et
al. [1]}
introduced a highly influential class of neighborhood aggregation
kernels for graphs with discrete labels based on the 1-dimensional
Weisfeiler-Lehman (1-WL) or color refinement
algorithm [2]}: a well-known heuristic for the
graph isomorphism problem.
Our approach can be thought of as an implementation of the WL kernel for
weighted networks by using the weighted quiver homology.
